OpenRaiser/Intern-Atlas huggingface dataset:
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates
Venue: arXiv cs.LG 2021
Year: 2021

An increasingly popular method for solving a constrained combinatorial optimisation problem is to first convert it into a quadratic unconstrained binary optimisation (QUBO) problem, and solve it using a standard QUBO solver. However, this relaxation introduces hyper-parameters that balance the objective and penalty terms for the constraints, and their chosen values significantly impact performance. Hence, tuning these parameters is an important problem. Existing generic hyper-parameter tuning methods require multiple expensive calls to a QUBO solver, making them impractical for performance critical applications when repeated solutions of similar combinatorial optimisation problems are required. In this paper, we propose the QROSS method, in which we build surrogate models of QUBO solvers via learning from solver data on a collection of instances of a problem. In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce good choices of penalty parameters with fewer number of calls to the QUBO solver. We take the Traveling Salesman Problem (TSP) as a case study, where we demonstrate that our method can find better solutions with fewer calls to QUBO solver compared with conventional hyper-parameter tuning techniques. Moreover, with simple adaptation methods, QROSS is shown to generalise well to out-of-distribution datasets and different types of QUBO solvers.
Weakly Complete Semantics Based on Undecidedness Blocking
Venue: arXiv cs.LO 2021
Year: 2021

In this paper we introduce a novel family of semantics called weakly complete semantics. Differently from Dung's complete semantics, weakly complete semantics employs a mechanism called undecidedness blocking by which the label undecided of an attacking argument is not always propagated to an otherwise accepted attacked argument. The new semantics are conflict-free, non-admissible but employing a weaker notion of admissibility; they allow reinstatement and they retain the majority of properties of complete semantics. We show how both weakly complete and Dung's complete semantics can be generated by applying different undecidedness blocking strategies, making undecidedness blocking a unifying mechanism underlying argumentation semantics. The semantics are also an example of ambiguity blocking Dunganian semantics and the first semantics to tackle the problem of self-defeating attacking arguments. In the last part of the paper we compare weakly complete semantics with the recent work of Baumann et al. on weakly admissible semantics. Since the two families of semantics do not coincide, a principle-based analysis of the two approaches is provided. The analysis shows how our semantics satisfy a number of principles satisfied by Dung's complete semantics but not by Baumann et al. semantics, including directionality, abstention, SCC-decomposability and cardinality of extensions, making them a more faithful non-admissible version of Dung' semantics.
How Social Are Social Media The Dark Patterns In Facebook's Interface
Venue: arXiv cs.HC 2021
Year: 2021

Many researchers have been concerned with social media and possible negative impacts on the well-being of their audience. With the popularity of social networking sites (SNS) steadily increasing, psychological and social sciences have shown great interest in their effects and consequences on humans. Unfortunately, it appears to be difficult to find correlations between SNS and the results of their works. We, therefore, investigate Facebook using the tools of HCI to find connections between interface features and the concerns raised by these domains. With a nod towards Dark Patterns, we use an empirical design analysis to identify interface interferences that impact users' online privacy. We further discuss how HCI can help to work towards more ethical user interfaces in the future.
SoPrA: Fabrication & Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing
Venue: arXiv cs.RO 2021
Year: 2021

Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and considered the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator's mass and the material properties, and predicts in real-time the soft robot's behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model's and sensor's accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.
MuRIL: Multilingual Representations for Indian Languages
Venue: arXiv cs.CL 2021
Year: 2021

India is a multilingual society with 1369 rationalized languages and dialects being spoken across the country (INDIA, 2011). Of these, the 22 scheduled languages have a staggering total of 1.17 billion speakers and 121 languages have more than 10,000 speakers (INDIA, 2011). India also has the second largest (and an ever growing) digital footprint (Statista, 2020). Despite this, today's state-of-the-art multilingual systems perform suboptimally on Indian (IN) languages. This can be explained by the fact that multilingual language models (LMs) are often trained on 100+ languages together, leading to a small representation of IN languages in their vocabulary and training data. Multilingual LMs are substantially less effective in resource-lean scenarios (Wu and Dredze, 2020; Lauscher et al., 2020), as limited data doesn't help capture the various nuances of a language. One also commonly observes IN language text transliterated to Latin or code-mixed with English, especially in informal settings (for example, on social media platforms) (Rijhwani et al., 2017). This phenomenon is not adequately handled by current state-of-the-art multilingual LMs. To address the aforementioned gaps, we propose MuRIL, a multilingual LM specifically built for IN languages. MuRIL is trained on significantly large amounts of IN text corpora only. We explicitly augment monolingual text corpora with both translated and transliterated document pairs, that serve as supervised cross-lingual signals in training. MuRIL significantly outperforms multilingual BERT (mBERT) on all tasks in the challenging cross-lingual XTREME benchmark (Hu et al., 2020). We also present results on transliterated (native to Latin script) test sets of the chosen datasets and demonstrate the efficacy of MuRIL in handling transliterated data.
Detecting and Understanding Branching Frequency Changes in Process Models
Venue: arXiv cs.IR 2021
Year: 2021

Business processes are continuously evolving in order to adapt to changes due to various factors. One type of process changes are branching frequency changes, which are related to changes in frequencies between different options when there is an exclusive choice. Existing methods either cannot detect such changes or cannot provide accurate and comprehensive results. In this paper, we propose a method which takes both event logs and process models as input and generates a choice sequence for each exclusive choice in the process model. The method then identifies change points based on the choice sequences. We evaluate our method on a real-life event log. Results show that our method can identify branching frequency changes in process models and provide comprehensive results to users.
Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring
Venue: arXiv cs.NE 2021
Year: 2021

One of the most important lessons from the success of deep learning is that learned representations tend to perform much better at any task compared to representations we design by hand. Yet evolution of evolvability algorithms, which aim to automatically learn good genetic representations, have received relatively little attention, perhaps because of the large amount of computational power they require. The recent method Evolvability ES allows direct selection for evolvability with little computation. However, it can only be used to solve problems where evolvability and task performance are aligned. We propose Quality Evolvability ES, a method that simultaneously optimizes for task performance and evolvability and without this restriction. Our proposed approach Quality Evolvability has similar motivation to Quality Diversity algorithms, but with some important differences. While Quality Diversity aims to find an archive of diverse and well-performing, but potentially genetically distant individuals, Quality Evolvability aims to find a single individual with a diverse and well-performing distribution of offspring. By doing so Quality Evolvability is forced to discover more evolvable representations. We demonstrate on robotic locomotion control tasks that Quality Evolvability ES, similarly to Quality Diversity methods, can learn faster than objective-based methods and can handle deceptive problems.
Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces
Venue: arXiv cs.HC 2021
Year: 2021

Self-adaptation approaches usually rely on closed-loop controllers that avoid human intervention from adaptation. While such fully automated approaches have proven successful in many application domains, there are situations where human involvement in the adaptation process is beneficial or even necessary. For such "human-in-the-loop" adaptive systems, two major challenges, namely transparency and controllability, have to be addressed to include the human in the self-adaptation loop. Transparency means that relevant context information about the adaptive systems and its context is represented based on a digital twin enabling the human an immersive and realistic view. Concerning controllability, the decision-making and adaptation operations should be managed in a natural and interactive way. As existing human-in-the-loop adaptation approaches do not fully cover these aspects, we investigate alternative human-in-the-loop strategies by using a combination of digital twins and virtual reality (VR) interfaces. Based on the concept of the digital twin, we represent a self-adaptive system and its respective context in a virtual environment. With the help of a VR interface, we support an immersive and realistic human involvement in the self-adaptation loop by mirroring the physical entities of the real world to the VR interface. For integrating the human in the decision-making and adaptation process, we have implemented and analyzed two different human-in-the-loop strategies in VR: a procedural control where the human can control the decision making-process and adaptations through VR interactions (human-controlled) and a declarative control where the human specifies the goal state and the configuration is delegated to an AI planner (mixed-initiative). We illustrate and evaluate our approach based on an autonomic robot system that is accessible and controlled through a VR interface.
A systematic association of subgraph counts over a network
Venue: arXiv cs.DM 2021
Year: 2021

We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings.
Prediction of progressive lens performance from neural network simulations
Venue: arXiv cs.LG 2021
Year: 2021

Purpose: The purpose of this study is to present a framework to predict visual acuity (VA) based on a convolutional neural network (CNN) and to further to compare PAL designs. Method: A simple two hidden layer CNN was trained to classify the gap orientations of Landolt Cs by combining the feature extraction abilities of a CNN with psychophysical staircase methods. The simulation was validated regarding its predictability of clinical VA from induced spherical defocus (between +/-1.5 D, step size: 0.5 D) from 39 subjectively measured eyes. Afterwards, a simulation for a presbyopic eye corrected by either a generic hard or a soft PAL design (addition power: 2.5 D) was performed including lower and higher order aberrations. Result: The validation revealed consistent offset of +0.20 logMAR +/-0.035 logMAR from simulated VA. Bland-Altman analysis from offset-corrected results showed limits of agreement (+/-1.96 SD) of -0.08 logMAR and +0.07 logMAR, which is comparable to clinical repeatability of VA assessment. The application of the simulation for PALs confirmed a bigger far zone for generic hard design but did not reveal zone width differences for the intermediate or near zone. Furthermore, a horizontal area of better VA at the mid of the PAL was found, which confirms the importance for realistic performance simulations using object-based aberration and physiological performance measures as VA. Conclusion: The proposed holistic simulation tool was shown to act as an accurate model for subjective visual performance. Further, the simulations application for PALs indicated its potential as an effective method to compare visual performance of different optical designs. Moreover, the simulation provides the basis to incorporate neural aspects of visual perception and thus simulate the VA including neural processing in future.
A new method for constructing linear codes with small hulls
Venue: arXiv cs.IT 2021
Year: 2021

The hull of a linear code over finite fields is the intersection of the code and its dual, which was introduced by Assmus and Key. In this paper, we develop a method to construct linear codes with trivial hull ( LCD codes) and one-dimensional hull by employing the positive characteristic analogues of Gauss sums. These codes are quasi-abelian, and sometimes doubly circulant. Some sufficient conditions for a linear code to be an LCD code (resp. a linear code with one-dimensional hull) are presented. It is worth mentioning that we present a lower bound on the minimum distances of the constructed linear codes. As an application, using these conditions, we obtain some optimal or almost optimal LCD codes (resp. linear codes with one-dimensional hull) with respect to the online Database of Grassl.
Performance Analysis of Deep Learning Workloads on a Composable System
Venue: arXiv cs.DC 2021
Year: 2021

A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix and match' resources dynamically allows for experimentation early in the design cycle, prior to the final architectural design or hardware implementation of a system. This design provides flexibility to serve a variety of workloads and provides a dynamic co-design platform that allows experiments and measurements in a controlled manner. For instance, key performance bottlenecks can be revealed early on in the experimentation phase thus avoiding costly and time consuming mistakes. Additionally, various system-level topologies can be evaluated when experimenting with new System on Chip (SoCs) and new accelerator types. This paper details the design of an enterprise composable infrastructure that we have implemented and made available to our partners in the IBM Research AI Hardware Center (AIHC). Our experimental evaluations on the composable system give insights into how the system works and evaluates the impact of various resource aggregations and reconfigurations on representative deep learning benchmarks.
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation
Venue: arXiv cs.CL 2021
Year: 2021

The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information content shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago, where we replace human annotators with language models. We also view these metrics as an extension of BLANC, a recently proposed approach to summary quality measurement based on the performance of a language model with and without the help of a summary. Using transformer based language models, we empirically verify that our metrics achieve state-of-the-art correlation with human judgement of the summary quality dimensions of both coherence and relevance, as well as competitive correlation with human judgement of consistency and fluency.
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Venue: arXiv cs.LG 2021
Year: 2021

Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach spaces. However, despite their remarkable early promise, they typically require large training data-sets consisting of paired input-output observations which may be expensive to obtain, while their predictions may not be consistent with the underlying physical principles that generated the observed data. In this work, we propose a novel model class coined as physics-informed DeepONets, which introduces an effective regularization mechanism for biasing the outputs of DeepOnet models towards ensuring physical consistency. This is accomplished by leveraging automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. We demonstrate that this simple, yet remarkably effective extension can not only yield a significant improvement in the predictive accuracy of DeepOnets, but also greatly reduce the need for large training data-sets. To this end, a remarkable observation is that physics-informed DeepONets are capable of solving parametric partial differential equations (PDEs) without any paired input-output observations, except for a set of given initial or boundary conditions. We illustrate the effectiveness of the proposed framework through a series of comprehensive numerical studies across various types of PDEs. Strikingly, a trained physics informed DeepOnet model can predict the solution of $\mathcal{O}(10^3)$ time-dependent PDEs in a fraction of a second -- up to three orders of magnitude faster compared a conventional PDE solver. The data and code accompanying this manuscript are publicly available at \url{https://github.com/PredictiveIntelligenceLab/Physics-informed-DeepONets}.
Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study
Venue: arXiv cs.SI 2021
Year: 2021

Background: Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one's confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective: We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods: First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results: We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions: We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions
Venue: arXiv cs.CR 2021
Year: 2021

Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment. Automatic reverse engineering of the toolchains behind these adversarial machine learning attacks will aid in recovering the tools and processes used in these attacks. In this paper, we present two techniques that support automated identification and attribution of adversarial ML attack toolchains using Co-occurrence Pixel statistics and Laplacian Residuals. Our experiments show that the proposed techniques can identify parameters used to generate adversarial samples. To the best of our knowledge, this is the first approach to attribute gradient based adversarial attacks and estimate their parameters. Source code and data is available at: https://github.com/michael-goebel/ei_red
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Venue: arXiv cs.CL 2021
Year: 2021

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence is available from https://github.com/drgriffis/text-essence.